Discovery of liquid crystalline polymers with high thermal conductivity using machine learning
Abstract Next-generation power electronics require efficient heat dissipation management, and molecular design guidelines are needed to develop polymers with high thermal conductivity. Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01671-w |
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| author | Hayato Maeda Stephen Wu Rika Marui Erina Yoshida Kan Hatakeyama-Sato Yuta Nabae Shiori Nakagawa Meguya Ryu Ryohei Ishige Yoh Noguchi Yoshihiro Hayashi Masashi Ishii Isao Kuwajima Felix Jiang Xuan Thang Vu Sven Ingebrandt Masatoshi Tokita Junko Morikawa Ryo Yoshida Teruaki Hayakawa |
| author_facet | Hayato Maeda Stephen Wu Rika Marui Erina Yoshida Kan Hatakeyama-Sato Yuta Nabae Shiori Nakagawa Meguya Ryu Ryohei Ishige Yoh Noguchi Yoshihiro Hayashi Masashi Ishii Isao Kuwajima Felix Jiang Xuan Thang Vu Sven Ingebrandt Masatoshi Tokita Junko Morikawa Ryo Yoshida Teruaki Hayakawa |
| author_sort | Hayato Maeda |
| collection | DOAJ |
| description | Abstract Next-generation power electronics require efficient heat dissipation management, and molecular design guidelines are needed to develop polymers with high thermal conductivity. Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region. The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity, but the molecular design of such polymers remains largely empirical. In this study, we developed a machine learning model that predicts with more than 96% accuracy whether liquid crystalline states will form based on the chemical structure of the polymer. By exploring the inverse mapping of this model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. The polymers were then experimentally synthesized, and the results confirmed that they form liquid crystalline phases, with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26 W m−1 K−1. |
| format | Article |
| id | doaj-art-b097e997be284320b9dd2ebdb679799d |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-b097e997be284320b9dd2ebdb679799d2025-08-20T03:45:34ZengNature Portfolionpj Computational Materials2057-39602025-07-011111910.1038/s41524-025-01671-wDiscovery of liquid crystalline polymers with high thermal conductivity using machine learningHayato Maeda0Stephen Wu1Rika Marui2Erina Yoshida3Kan Hatakeyama-Sato4Yuta Nabae5Shiori Nakagawa6Meguya Ryu7Ryohei Ishige8Yoh Noguchi9Yoshihiro Hayashi10Masashi Ishii11Isao Kuwajima12Felix Jiang13Xuan Thang Vu14Sven Ingebrandt15Masatoshi Tokita16Junko Morikawa17Ryo Yoshida18Teruaki Hayakawa19School of Materials and Chemical Technology, Institute of Science TokyoThe Institute of Statistical Mathematics, Research Organization of Information and SystemsSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoThe Institute of Statistical Mathematics, Research Organization of Information and SystemsThe Institute of Statistical Mathematics, Research Organization of Information and SystemsNational Institute for Materials ScienceNational Institute for Materials ScienceInstitute of Materials in Electrical Engineering 1, RWTH Aachen UniversityInstitute of Materials in Electrical Engineering 1, RWTH Aachen UniversityInstitute of Materials in Electrical Engineering 1, RWTH Aachen UniversitySchool of Materials and Chemical Technology, Institute of Science TokyoSchool of Materials and Chemical Technology, Institute of Science TokyoThe Institute of Statistical Mathematics, Research Organization of Information and SystemsSchool of Materials and Chemical Technology, Institute of Science TokyoAbstract Next-generation power electronics require efficient heat dissipation management, and molecular design guidelines are needed to develop polymers with high thermal conductivity. Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region. The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity, but the molecular design of such polymers remains largely empirical. In this study, we developed a machine learning model that predicts with more than 96% accuracy whether liquid crystalline states will form based on the chemical structure of the polymer. By exploring the inverse mapping of this model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. The polymers were then experimentally synthesized, and the results confirmed that they form liquid crystalline phases, with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26 W m−1 K−1.https://doi.org/10.1038/s41524-025-01671-w |
| spellingShingle | Hayato Maeda Stephen Wu Rika Marui Erina Yoshida Kan Hatakeyama-Sato Yuta Nabae Shiori Nakagawa Meguya Ryu Ryohei Ishige Yoh Noguchi Yoshihiro Hayashi Masashi Ishii Isao Kuwajima Felix Jiang Xuan Thang Vu Sven Ingebrandt Masatoshi Tokita Junko Morikawa Ryo Yoshida Teruaki Hayakawa Discovery of liquid crystalline polymers with high thermal conductivity using machine learning npj Computational Materials |
| title | Discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| title_full | Discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| title_fullStr | Discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| title_full_unstemmed | Discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| title_short | Discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| title_sort | discovery of liquid crystalline polymers with high thermal conductivity using machine learning |
| url | https://doi.org/10.1038/s41524-025-01671-w |
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